Amenities

These are amenities which currently include reserves and gold course. Trails and Beaches have their own page.

Golf Courses

Code

source("_common.R")

golf=read_sf("/Users/ashleylowe/Trails/golf_courses.shp/golf_courses.shp")
golf=st_transform(golf, 4326 )
golf=golf[c(1,7,8,12)]


tmap_mode("view")

center_lon <- -157.45
center_lat <- 20.75
zoom_level <- 7

tm_shape(golf) +
  tm_dots(
    col = "Name",
    palette = "magma",
    size = 0.1,
    id = "Name",
    popup.vars = c("Name" = "Name"),
    legend.show = FALSE      # hides the legend
  ) +
  tm_basemap() +
  tm_view(set.view = c(center_lon, center_lat, zoom_level))

Distance Between Points

Distance to each Property calculation

Code

# 1. find nearest feature index
nearest_idx <- st_nearest_feature(PB_HawaiiInfo, golf)

# 2. get nearest trails (same row order as PB_HawaiiInfo)
nearest_trails <- golf[nearest_idx, ]

# 3. calculate distance (element-wise)
nearest_dist_m <- st_distance(PB_HawaiiInfo, nearest_trails, by_element = TRUE)

# 4. merge trail attributes + distance into PB_HawaiiInfo
PB_HawaiiInfo <- PB_HawaiiInfo %>%
  mutate(
    nearest_golf = as.numeric(nearest_dist_m)
  ) %>%
  bind_cols(st_drop_geometry(nearest_trails))  # adds trail attributes

Nature Reserves

Centralized location of managed lands protecting natural and cultural resources of the Main Hawaiian Islands and Kure Atoll.

We will use this one to create a buffer and identify the properties that adjoin a reserve.

Code

reserves <- read_sf("/Users/ashleylowe/Trails/Reserves/Reserves.shp")
reserves$managedby <- gsub("[()]", "", reserves$managedby)

tmap_mode("view")

center_lon <- -157.45
center_lat <- 20.75
zoom_level <- 7

tm_shape(reserves) +
  tm_polygons(
    "managedby",
    palette = "magma",
    id = "name",
    popup.vars = c("Managed By" = "managedby"),
    legend.show = FALSE      # hides the legend
  ) +
  tm_basemap() +
  tm_view(set.view = c(center_lon, center_lat, zoom_level))

Matching polygons

To adjoin we will focus on matching with buffer of the reserve location and use the tax plot match to adjoin to the property data. Add a buffer distance of 10 meters and then match with the Hawaii tmz file.

Code

reserves <- st_transform(reserves, 32605)
reserves_buffered <- st_buffer(reserves, dist = 10)
reserves_buffered <- st_transform(reserves_buffered, 4326)
intersections <- st_intersects(HI_2023_shapefile, reserves_buffered)
HI_intersections <- HI_2023_shapefile[lengths(intersections) > 0, ]



ggplot() +
    geom_sf(data = HI_intersections, fill = "brown", color = "grey") +
    geom_sf(data = reserves_buffered, fill = "forestgreen", color = "black") +
  geom_sf(data = coastline, color = "black") +
    coord_sf(
        xlim = c(-158.5, -157.5),  # longitudes
        ylim = c(21, 21.9),        # latitudes in correct order
        expand = FALSE
    ) +
    theme_minimal() +
    labs(title = "Buffered Reserves (Green) and Adjoining Properties (Brown)")

Following code to match each adjoin reserve to property. Property can adjoin multiple reserves names so we combine them by comma in one column.

Code

# Create a vector of indices for the first overlapping shp2 feature per shp1 row
HI_2023_shapefile$reserve_names <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$name[idx], collapse = ", ")
  }
})

HI_2023_shapefile$managedby <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$managedby[idx], collapse = ", ")
  }
})

HI_2023_shapefile$type_defin <- sapply(intersections, function(idx) {
  if (length(idx) == 0) {
    return(NA_character_)
  } else {
    paste(reserves_buffered$type_defin[idx], collapse = ", ")
  }
})